论文标题
在爱因斯坦的有效粘度公式上
On Einstein's effective viscosity formula
论文作者
论文摘要
在他的博士学位论文中,爱因斯坦(Einstein)得出了明确的一阶膨胀,以使stokes流体的有效粘度在低密度下悬浮液的悬浮液。他的形式派生依赖于两个隐式假设:(i)粒子的大小与观察量表之间存在尺度分离; (ii)一阶,稀释颗粒不相互作用。用数学术语来说,第一个假设等于均质化结果的有效性定义了有效的粘度张量,这现在已被充分理解。接下来,第二个假设使爱因斯坦通过将颗粒视为分离,以低密度的近似于这种有效的粘度。实际上,严格的理由是非常微妙的,因为有效的粘度是颗粒合奏的非线性非局部功能,并且流体动力相互作用具有边缘性可合转性。在本回忆录中,我们在最一般的环境中建立了爱因斯坦的有效粘度公式。此外,我们以群集扩展的形式追求对任意顺序的低密度扩展,其中流体动力相互作用的总和至关重要地需要合适的重降低。特别是,我们在二阶校正中通过Batchelor和Green证明了一个著名的结果,我们首次明确描述了所有高级重量级化。在某些特定的设置中,我们进一步解决了整个集群扩展的总结性。我们的方法依赖于组合参数,变分分析,椭圆规律性,概率理论和图解整合方法的结合。
In his PhD thesis, Einstein derived an explicit first-order expansion for the effective viscosity of a Stokes fluid with a suspension of small rigid particles at low density. His formal derivation relied on two implicit assumptions: (i) there is a scale separation between the size of the particles and the observation scale; and (ii) at first order, dilute particles do not interact with one another. In mathematical terms, the first assumption amounts to the validity of a homogenization result defining the effective viscosity tensor, which is now well understood. Next, the second assumption allowed Einstein to approximate this effective viscosity at low density by considering particles as being isolated. The rigorous justification is, in fact, quite subtle as the effective viscosity is a nonlinear nonlocal function of the ensemble of particles and as hydrodynamic interactions have borderline integrability. In the present memoir, we establish Einstein's effective viscosity formula in the most general setting. In addition, we pursue the low-density expansion to arbitrary order in form of a cluster expansion, where the summation of hydrodynamic interactions crucially requires suitable renormalizations. In particular, we justify a celebrated result by Batchelor and Green on the second-order correction and we explicitly describe all higher-order renormalizations for the first time. In some specific settings, we further address the summability of the whole cluster expansion. Our approach relies on a combination of combinatorial arguments, variational analysis, elliptic regularity, probability theory, and diagrammatic integration methods.